Natural Language Processing (NLP) and computational linguistic techniques are increasingly being applied across various domains, yet their use in legal and regulatory tasks remains limited. To address this gap, we develop an efficient bilingual question-answering framework for regulatory documents, specifically the Bangladesh Police Gazettes, which contain both English and Bangla text. Our approach employs modern Retrieval Augmented Generation (RAG) pipelines to enhance information retrieval and response generation. In addition to conventional RAG pipelines, we propose an advanced RAG-based approach that improves retrieval performance, leading to more precise answers. This system enables efficient searching for specific government legal notices, making legal information more accessible. We evaluate both our proposed and conventional RAG systems on a diverse test set on Bangladesh Police Gazettes, demonstrating that our approach consistently outperforms existing methods across all evaluation metrics.

Crime is an unlawful act that carries legal repercussions. Bangladesh has a high crime rate due to poverty, population growth, and many other socio-economic issues. For law enforcement agencies, understanding crime patterns is essential for preventing future criminal activity. For this purpose, these agencies need structured crime database. This paper introduces a novel crime dataset that contains temporal, geographic, weather, and demographic data about 6574 crime incidents of Bangladesh. We manually gather crime news articles of a seven year time span from a daily newspaper archive. We extract basic features from these raw text. Using these basic features, we then consult standard service-providers of geo-location and weather data in order to garner these information related to the collected crime incidents. Furthermore, we collect demographic information from Bangladesh National Census data. All these information are combined that results in a standard machine learning dataset. Together, 36 features are engineered for the crime prediction task. Five supervised machine learning classification algorithms are then evaluated on this newly built dataset and satisfactory results are achieved. We also conduct exploratory analysis on various aspects the dataset. This dataset is expected to serve as the foundation for crime incidence prediction systems for Bangladesh and other countries. The findings of this study will help law enforcement agencies to forecast and contain crime as well as to ensure optimal resource allocation for crime patrol and prevention.

New Delhi, October 4,2019:Tech Mahindra, a leading provider of digital transformation, consulting and business reengineering services and solutions, has signed a Memorandum of Understanding(MoU) with Startup Bangladesh to foster the growth of digital startup ecosystem in Bangladesh, by providing guidance and mentoring to the budding entrepreneurs.The MoU was signed in presence of H.E. Sheikh Hasina, Prime Minister of Bangladesh and Shri Piyush Goyal - Minister of Railways and Commerce & Industry, Government of India. As part of the comprehensive growth framework outlined within the MoU, Tech Mahindra will be assisting new-age technology startups in the country, focusing on future technologies like Artificial Intelligence, 5G, Big Data, Cybersecurity, Blockchain, Internet of Things (IoT) and Machine Learning, to leverage digital growth opportunities across its global network. Startup Bangladeshis a concrete initiative by the Government of Bangladesh to create new opportunities, develop technical skills and help realize the vision of Digital Bangladesh. As part of the MoU, Tech Mahindra will extend collaboration opportunities to the innovators of Startup Bangladesh to engage with its research and development arm Makers Lab, which has global footprint including India, US, Europe and Australia. This collaboration will take up initiatives like Ideathons and Hackathons across educational institutions in Bangladesh.

Browsing and finding relevant information for Bangladeshi laws is a challenge faced by all law students and researchers in Bangladesh, and by citizens who want to learn about any legal procedure. Some law archives in Bangladesh are digitized, but lack proper tools to organize the data meaningfully. We present a text visualization tool that utilizes machine learning techniques to make the searching of laws quicker and easier. Using Doc2Vec to layout law article nodes, link mining techniques to visualize relevant citation networks, and named entity recognition to quickly find relevant sections in long law articles, our tool provides a faster and better search experience to the users. Qualitative feedback from law researchers, students, and government officials show promise for visually intuitive search tools in the context of governmental, legal, and constitutional data in developing countries, where digitized data does not necessarily pave the way towards an easy access to information.